New developments in paleo-ecotoxicology: Emerging approaches in applying lake sediment archives to assess impacts from aquatic pollution
Bibliographic record
Abstract
Many aquatic ecosystems have experienced a history of impacts from exposure to environmental contaminants, with aquatic biota often exhibiting population and/or community changes following toxicant exposure. However, identifying aquatic effects can be challenging due to a paucity of monitoring data and gaps in monitoring records. The study of lake sediments as natural environmental archives (paleolimnology) provides an opportunity to assess the long-term impacts of pollution on aquatic organisms and ecosystems. Cores collected from polluted systems can preserve both a record of the toxicant(s) in question, and also an archive of biotic changes at multiple scales of organization, from molecular levels to communities to ecosystem processes (paleo-ecotoxicology). Here, we review recent examples of the application of the paleo-ecotoxicological approach based on ‘classical’ paleolimnological indicators of contamination (e.g. metals, organic contaminants) and ecotoxicological response variables (e.g. pigments and subfossil morphological indicators). We also outline emerging environmental contaminants (e.g. microplastics, pharmaceuticals) that are increasingly being detected and documented in sediment records, highlighting evolving sources and intensities of toxicological stressors that are fruitful areas for future paleo-ecotoxicological inquiry. In addition, the ability to characterize aquatic community changes using emerging sedimentary DNA approaches has rapidly expanded, providing a powerful opportunity to build on the ecological information obtained from traditional indicators to better understand ecosystem responses to contamination. Finally, we argue for the importance of methodological approaches that specifically examine the ecotoxicological impacts of contamination in the future application of the paleo-ecotoxicological approach by exploring several recent examples.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".